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Big data and new knowledge in medicine: the thinking, training, and tools needed for a learning health system.
Open accessAbstract
Big data in medicine--massive quantities of health care data accumulating from patients and populations and the advanced analytics that can give those data meaning--hold the prospect of becoming an engine for the knowledge generation that is necessary to address the extensive unmet information needs of patients, clinicians, administrators, researchers, and health policy makers. This article explores the ways in which big data can be harnessed to advance prediction, performance, discovery, and comparative effectiveness research to address the complexity of patients, populations, and organizations. Incorporating big data and next-generation analytics into clinical and population health research and practice will require not only new data sources but also new thinking, training, and tools. Adequately utilized, these reservoirs of data can be a practically inexhaustible source of knowledge to fuel a learning health care system.
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“This study demonstrates that deployment governance can be treated as a first class computational problem in clinical artificial intelligence systems, distinct from model development and validation [8-11].”
From Adaptive FHIR Native AI Governance for Clinical Decision Support: A Modular, Auditable Deployment Framework for Real World Clinical AI (2026) · cites this paper as [9]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Robotic-assisted surgeries provide unparalleled precision, stability, and skill, reducing the risk of complications and accelerating patient recovery times.”
From Ethical implications of AI and robotics in healthcare: A review (2023) · cites this paper as [15]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Surgeons can perform minimally invasive procedures with greater accuracy.[15]”
From Ethical implications of AI and robotics in healthcare: A review (2023) · cites this paper as [15]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
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“Current monitoring practices are often reactive, retrospective, or limited to aggregate performance metrics, offering limited support for real time governance or accountability [8, 9].”
From Adaptive FHIR Native AI Governance for Clinical Decision Support: A Modular, Auditable Deployment Framework for Real World Clinical AI (2026) · cites this paper as [9]
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In the citing paper
“The framework is organized into three interacting layers: a data layer, a governance layer, and an execution layer. This modular separation is intended to decouple clinical data representation, deployment decision logic, and predictive model execution, enabling independent evaluation and reuse of each component [9-11].”
From Adaptive FHIR Native AI Governance for Clinical Decision Support: A Modular, Auditable Deployment Framework for Real World Clinical AI (2026) · cites this paper as [9]
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Metadata
- Authors
- Krumholz, H. M.
- Affiliations
- Yale University (US)
- Corresponding author
- Krumholz, Harlan M.
- ORCID
- Krumholz, Harlan M.
- Venue
- Health Affairs
- Publisher
- Health Affairs Publishing, LLC
- Published
- 2014-07
- Volume
- 33
- Issue
- 7
- Pages
- 1163-1170
- ISSN
- 0278-2715 (ISSN-L)
- 1544-5208
- PMC
- PMC5459394
- OpenAlex
- W2027540165
- Cited by
- 589
- References
- 19
- Citations by year
- 2026: 18 · 2025: 31 · 2024: 41 · 2023: 30 · 2022: 41 · 2021: 52 · 2020: 68 · 2019: 61 · 2018: 126 · 2017: 47 · 2016: 36 · 2015: 32 · 2014: 5
- FWCI
- 74.93
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Health Affairs (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Heart, Lung, and Blood Institute (U01HL105270)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- big data in healthcare, learning health systems, health care analytics, comparative effectiveness research, prediction, clinical research, population health, health data sources, clinician training, health policy
- MeSH terms
- Data Interpretation, Statistical, Delivery of Health Care, Humans, Learning, Medical Informatics, Thinking, Decision Support Systems, Clinical, Data Mining, Datasets as Topic
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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